📘 The experiment tracker for foundation model training

Real-time Web Dashboard for Optuna.

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

Python library for CMA Evolution Strategy.

ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.

Free trading system for crypto exchanges SPOT market. Adaptive customizable reverse grid strategy based on martingale.

PyTorch implementation of over 30 realtime semantic segmentations models, e.g. BiSeNetv1, BiSeNetv2, CGNet, ContextNet, DABNet, DDRNet, EDANet, ENet, ERFNet, ESPNet, ESPNetv2, FastSCNN, ICNet, LEDNet, LinkNet, PP-LiteSeg, SegNet, ShelfNet, STDC, SwiftNet, and support knowledge distillation, distributed training, Optuna etc.

Make GNN easy to start with

Code repository for the online course Hyperparameter Optimization for Machine Learning

QSARtuna: QSAR model building with the optuna framework

📝 Examples of how to use Neptune for different use cases and with various MLOps tools

🔍 Lookup classes and instantiate them with style

PyTorch-Lightning Library for Neural News Recommendation

Going beyond BEDMAP2 using a super resolution deep neural network. Also a convenient flat file data repository for high resolution bed elevation datasets around Antarctica.

⚡️ AllenNLP plugin for adding subcommands to use Optuna, making hyperparameter optimization easy

PyTorch tutorial for using RNN and Encoder-Decoder RNN for time series forecasting

OptKeras: wrapper around Keras and Optuna for hyperparameter optimization

Train, evaluate, and optimize implicit feedback-based recommender systems.

Tutorial on training a PyTorch neural network model using MLflow for experiment tracking & Optuna for hyperparameter optimization.